Determining sources of erroneous downhole predictions

ABSTRACT

A system usable in a wellbore can include a processing device and a memory device in which instructions executable by the processing device are stored for causing the processing device to: generate multiple predicted values of a first parameter associated with a well environment or a wellbore operation; determine a first trend indicated by the multiple predicted values; receive, from a sensor, multiple measured values of a second parameter associated with the well environment or the wellbore operation; determine a second trend indicated by the multiple measured values; determine a difference between the first trend and the second trend or a rate of change of the difference; and in response to the difference exceeding a threshold or the rate of change exceeding another threshold, determine a source of the difference including at least one of an erroneous user input, an equipment failure, a wellbore event, or a model error.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to International Patent Application No.PCT/US2015/047278 titled “Predicting Wellbore Operation Parameters”, andInternational Patent Application No. PCT/US2015/047287 titled “TuningPredictions of Wellbore Operation Parameters” both of which were filedon Aug. 27, 2015. The entirety of both applications are herebyincorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates generally to devices for use with wellsystems. More specifically, but not by way of limitation, thisdisclosure relates to a system for determining sources of erroneousdownhole predictions.

BACKGROUND

A well system (e.g., oil or gas wells for extracting fluid or gas from asubterranean formation) can include a wellbore. Various well tools canbe used for performing operations in the wellbore. It can be desirableto predict a characteristic or effect of a wellbore operation prior toperforming the wellbore operation. For example, it can be desirable topredict an amount of pressure generated by a drilling operation. It canbe challenging to accurately predict the characteristics of the wellboreoperation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a cross-sectional view of an example of a part of a wellsystem that includes a system for determining sources of erroneousdownhole predictions according to some aspects.

FIG. 2 is a cross-sectional view of an example of a well system thatincludes a system for determining sources of erroneous downholepredictions according to some aspects.

FIG. 3 is a block diagram of an example of a system for determiningsources of erroneous downhole predictions according to some aspects.

FIG. 4 is a flow chart of an example of a process for determiningdifferences between downhole predictions and measured values accordingto some aspects.

FIG. 5 is a graph depicting a probability-mass distribution generatedusing predicted equivalent circulating density (ECD) values according tosome aspects.

FIG. 6 is a graph depicting a probability-mass distribution generatedusing predicted stand pipe pressure (SPP) values according to someaspects.

FIG. 7 is a graph depicting a probability-mass distribution generatedusing predicted ECD values tuned for a rotary drilling wellboreoperation according to some aspects.

FIG. 8 is a graph depicting a probability-mass distribution generatedusing predicted ECD values tuned for a slide drilling wellbore operationaccording to some aspects.

FIG. 9 is a graph depicting a probability-mass distribution generatedusing predicted ECD values tuned for a circulating wellbore operationaccording to some aspects.

FIG. 10 is a graph depicting a probability-mass distribution generatedusing predicted ECD values tuned for a tripping-in wellbore operationaccording to some aspects.

FIG. 11 is a graph depicting a probability-mass distribution generatedusing predicted ECD values tuned for a tripping-out wellbore operationaccording to some aspects.

FIG. 12 is a graph depicting a probability-mass distribution generatedusing predicted ECD values tuned for an idle wellbore operationaccording to some aspects.

FIG. 13 is a graph depicting a probability-mass distribution generatedusing predicted SPP values tuned for a rotary drilling wellboreoperation according to some aspects.

FIG. 14 is a graph depicting a probability-mass distribution generatedusing predicted SPP values tuned for a slide drilling wellbore operationaccording to some aspects.

FIG. 15 is a graph depicting a probability-mass distribution generatedusing predicted SPP values tuned for a circulating wellbore operationaccording to some aspects.

FIG. 16 is a graph depicting a probability-mass distribution generatedusing predicted SPP values tuned for a tripping-in wellbore operationaccording to some aspects.

FIG. 17 is a graph depicting a probability-mass distribution generatedusing predicted SPP values tuned for a tripping-out wellbore operationaccording to some aspects.

FIG. 18 is a flow chart of an example of a process for determiningsources of erroneous downhole predictions according to some aspects.

FIG. 19 is a flow chart of an example of a process for determining asource of a difference between two trends according to some aspects.

DETAILED DESCRIPTION

Certain aspects and features of the present disclosure relate to asystem for determining a source of an erroneous predicted value of aparameter associated with an environmental condition in a wellbore orassociated with a wellbore operation. For example, the system cancompare the predicted value of the parameter to a measured value of theparameter (e.g., from a sensor) to determine a difference. If thedifference exceeds a threshold, the system can identify a source of thedisparity between the predicted value of the parameter and the measuredvalue of the parameter. As another example, the system can determine afirst trend indicated by multiple predicted values of the parameter anda second trend indicated by multiple measured values of the parameter.If the first trend and the second trend diverge (or converge), thesystem can identify a source of the divergence (or convergence).

In some examples, the system can determine if the source (e.g., of adisparity between one or more predicted values of the parameter and oneor more measured values of the parameter, or a particular trend)includes an event occurring in the wellbore. The event can include achange in the environmental condition in the wellbore, a well tooloperating in a specific manner, the well tool failing to operate in aparticular manner, or any combination of these. In some examples, thesystem can include a sensor proximate to the wellbore for measuring oneor more characteristics of the wellbore indicative of a wellbore event.The system can use data from the sensor to determine if the event isoccurring or has occurred.

The system can additionally or alternatively determine if the sourceincludes erroneous data input by a user or provided by a sensor. Theincorrect data can be used by a model executing on a computing device togenerate the predicted value of the parameter. In some examples, thesystem can compare the data input into the model with measured data fromone or more sensors to determine if there is a difference between thetwo. For example, the system can compare a predicted downhole pressurelevel input into the model to pressure data from a pressure sensor inthe wellbore to determine if there is a difference between the two. Insome examples, if there is a difference, the system can determine thatincorrect data was input into the model.

The system can additionally or alternatively determine if the sourceincludes an error in the model or an equipment failure. In someexamples, the system can output an error notification if the systemcannot identify the source. For example, the system can output the errornotification if the system determines that the source is not due to anevent occurring in the wellbore, incorrect data provided by a user,incorrect data provided by a sensor, an error in the model, an equipmentfailure, or any combination of these.

In some examples, the system can select and implement one or moreprocesses or tasks based on the source. The one or more processes ortasks can reduce the disparity between a predicted value of theparameter and a measured value of the parameter, alter a trend indicatedby multiple predicted values of the parameter and/or multiple measuredvalues of the parameter, or both. For example, if the difference betweena predicted value of the parameter and a measured value of the parameteris due to incorrect data input by the user, the system can prompt theuser for new data. The system can receive the new data from the user andapply the new data to the model. As another example, if the differencebetween the predicted value of the parameter and the measured value ofthe parameter is due to an event occurring in the wellbore, the systemcan modify a parameter of the model to account for the event, execute anew model, prompt the user for action, or any combination of these. Asstill another example, if the difference between the predicted value ofthe parameter and the measured value of the parameter is due to an errorin the model, the system can modify a parameter of the model, execute anew model, alert the user to the error in the model, or any combinationof these.

These illustrative examples are given to introduce the reader to thegeneral subject matter discussed here and are not intended to limit thescope of the disclosed concepts. The following sections describe variousadditional features and examples with reference to the drawings in whichlike numerals indicate like elements, and directional descriptions areused to describe the illustrative aspects but, like the illustrativeaspects, should not be used to limit the present disclosure.

FIG. 1 is a cross-sectional view of an example of a well system 100 thatincludes a system for determining sources of erroneous downholepredictions according to some aspects. In this example, the well system100 includes a wellbore extending through various earth strata. Thewellbore can extend through a hydrocarbon bearing subterraneanformation. In some examples, the wellbore can include a casing string116 and a cement sheath 124. In some examples, the cement sheath 124 cancouple the casing string 116 to a wall of the wellbore. In someexamples, the wellbore can include fluid 114. An example of the fluid114 can include mud. The fluid 114 can flow in an annulus 112 positionedbetween a well tool 101 and a wall of the casing string 116.

The well tool 101 can be positioned in the wellbore. In some examples,the well tool 101 is a drilling tool, such as a measuring-while-drillingtool. Examples of the drilling tool can include a logging-while-drillingtool, a pressure-while-drilling tool, a temperature-while-drilling tool,or any combination of these. The well tool 101 can include varioussubsystems 102, 104, 106, 107. For example, the well tool 101 caninclude a subsystem 102 that includes a communication subsystem. Thewell tool 101 can also include a subsystem 104 that includes a saversubsystem or a rotary steerable system. A tubular section or anintermediate subsystem 106 (e.g., a mud motor ormeasuring-while-drilling module) can be positioned between the othersubsystems 102, 104. The well tool 101 can include a drill bit 110 fordrilling the wellbore. The drill bit 110 can be coupled to anothertubular section or intermediate subsystem 107 (e.g., ameasuring-while-drilling module or a rotary steerable system). In someexamples, the well tool 101 can include tubular joints 108 a-b. Tubularjoints 108 a-b can allow the well tool 101 to bend or can couple variouswell tool subsystems 102, 104, 106 together.

The well system 100 includes one or more sensors 118 a, 118 c. Thesensors 118 a, 118 c can detect one or more parameters associated withan environment in the wellbore, a wellbore operation (e.g., theoperation of the well tool 101 in the wellbore), or both and transmit,via a wired or wireless interface, associated sensor data to a computingdevice 120. The sensors 118 a-b can be positioned in or on the well tool101, the casing string 116, the cement sheath 124, or elsewhere in thewell system. The sensors 118 a, 118 c can be of the same type or can bedifferent. Examples of the sensors 118 a, 118 c can include a pressuresensor, a temperature sensor, a microphone, an accelerometer, a depthsensor, a resistivity sensor, a vibration sensor, a fluid analyzer ordetector, an ultrasonic transducer, or any combination of these.

The computing device 120 can be positioned at the well surface, belowground, or offsite. The computing device 120 can include a processorinterfaced with other hardware via a bus. A memory, which can includeany suitable tangible (and non-transitory) computer-readable medium,such as RAM, ROM, EEPROM, or the like, can embody program componentsthat configure operation of the computing device 120. In some examples,the computing device 120 can include input/output interface components(e.g., a display, keyboard, touch-sensitive surface, and mouse) andadditional storage.

The computing device 120 can communicate with the sensors 118 a, 118 cvia a communication device 122. The communication device 122 canrepresent one or more of any components that facilitate a networkconnection. In the example shown in FIG. 1, the communication device 122is wireless and can include wireless interfaces such as IEEE 802.11,Bluetooth, or radio interfaces for accessing cellular telephone networks(e.g., transceiver/antenna for accessing a CDMA, GSM, UMTS, or othermobile communications network). In other examples, the communicationdevice 122 can be wired and can include interfaces such as Ethernet,USB, IEEE 1394, or a fiber optic interface. An example of the computingdevice 120 is described in greater detail with respect to FIG. 3.

In some examples, the computing device 120 can predict a value of aparameter associated with the environment in the wellbore or a wellboreoperation (e.g., operating the well tool 101 in the wellbore). Thewellbore operation can include running a tubular (e.g. pipe) into thewellbore, removing the tubular from the wellbore, circulating a fluid114 through the wellbore, cleaning the wellbore, making a connectionbetween two well system components (e.g., two well tools or tubulars), adrilling operation (e.g., slide drilling or rotary drilling), idling,and/or any other operation occurring in the wellbore. The sensors 118 a,118 c can measure the parameter and transmit associated sensor data tothe computing device 120. The computing device 120 can receive thesensor data and compare the predicted parameter to the measuredparameter to determine a difference between the two. The computingdevice 120 can plot a data point representative of the differencebetween the two on a graph, such as a probability-mass distributiongraph. The computing device 120 can iterate this process (e.g., in realtime) to plot multiple data points on the graph. In some examples, thecomputing device 120 can analyze the graph or the associated data pointsto determine the accuracy of one or more predicted values of parameters.

In some examples, the computing device 120 can determine a source of adifference between the predicted parameter and the measured parameter.For example, the computing device 120 can determine that the differenceis likely due to an event occurring in the wellbore, an error with amodel that generated the predicted value of the parameter, an error withdata input by a user, an equipment failure, or any combination of these.The computing device 120, the well operator, or both can perform one ormore processes or tasks based on the source of the difference. Theprocesses or tasks can be selected to correct the error or otherwisereduce the difference between the predicted parameter and the measuredparameter.

In some examples, the one or more sensors 118 a, 118 c can be positionedin other configurations. For example, referring to FIG. 2, a well system200 can include one or more sensors 118 a-b. The sensors 118 a-b can becoupled to a well tool 214 (e.g., a formation-testing tool) and/or acasing string 206. The well tool 214 can be conveyed into a wellbore 202via a wireline 210, slickline, or coiled tube. The wireline 210,slickline, or coiled tube can be wound around a reel 216 and guided intothe wellbore 202 using, for example, a guide 212 or winch.

The sensors 118 a-b can transmit data via a wired or wireless interfaceto the computing device 120. The computing device 120 can compare sensordata from the sensors 118 a-b with predicted parameters (of anenvironment in the wellbore or the operation of the well tool 214) todetermine differences between the two. The computing device 120 canoutput the differences via a visual user interface, such as on a graph.In some examples, the computing device 120 can determine a source of adifference between the sensor data and a predicted parameter. Thecomputing device 120, the well operator, or both can perform one or moreprocesses or tasks (based on the source of the difference) selected toreduce the difference between the sensor data and the predictedparameter.

FIG. 3 is a block diagram of an example of a system for determiningsources of erroneous downhole predictions according to some aspects. Insome examples, the components shown in FIG. 3 (e.g., the computingdevice 120, power source 320, display 310, and communication device 122)can be integrated into a single structure. For example, the componentscan be within a single housing. In other examples, the components shownin FIG. 3 can be distributed (e.g., in separate housings) and inelectrical communication with each other.

The computing device 120 can include a processor 304, a memory 308, anda bus 306. The processor 304 can execute one or more operations fordetermining sources of erroneous downhole predictions. The processor 304can execute instructions stored in the memory 308 to perform theoperations. The processor 304 can include one processing device ormultiple processing devices. Non-limiting examples of the processor 304include a Field-Programmable Gate Array (“FPGA”), anapplication-specific integrated circuit (“ASIC”), a microprocessor, etc.

The processor 304 can be communicatively coupled to the memory 308 viathe bus 306. The non-volatile memory 308 may include any type of memorydevice that retains stored information when powered off. Non-limitingexamples of the memory 308 include electrically erasable andprogrammable read-only memory (“EEPROM”), flash memory, or any othertype of non-volatile memory. In some examples, at least some of thememory 308 can include a medium from which the processor 304 can readinstructions. A computer-readable medium can include electronic,optical, magnetic, or other storage devices capable of providing theprocessor 304 with computer-readable instructions or other program code.Non-limiting examples of a computer-readable medium include (but are notlimited to) magnetic disk(s), memory chip(s), ROM, random-access memory(“RAM”), an ASIC, a configured processor, optical storage, or any othermedium from which a computer processor can read instructions. Theinstructions can include processor-specific instructions generated by acompiler or an interpreter from code written in any suitablecomputer-programming language, including, for example, C, C++, C #, etc.

In some examples, the memory 308 can include a model 312. The model 312can include one or more algorithms configured to predict a parameterassociated with an environment in a well system or a wellbore operation(e.g., an operation of a well tool). The model 312 can be tuned forspecific wellbore operations, such as rotary drilling, slide drilling,pulling a pipe out of a wellbore, running a pipe into the wellbore,circulating fluid through the well system, wellbore cleaning, or anycombination of these. In some examples, the model 312 can include theequation:ECD Value=Total Pressure/(0.052*TVD)where ECD Value is a predicted ECD value in ppg (pounds per gallon) at agiven depth for one or more fluids (e.g., Newtonian or Non-Newtonianfluids) in a wellbore; TVD (true vertical depth) is a vertical depth ofa wellbore in feet; and Total Pressure is in psi (pounds per squareinch). In some examples, the model 312 can include the followingequation for determining the Total Pressure:Total Pressure=Hydrostatic Pressure+Annular Pressure LossIn some examples, the model 312 can include the following equation fordetermining the Hydrostatic Pressure:Hydrostatic Pressure=0.052*Mud Weight*TVDwhere hydrostatic pressure is in psi; and Mud Weight is in ppg and canbe determined by applying temperature and pressure compressibility andexpansion effects to a surface Mud Weight. The surface Mud Weight can bemeasured using a mud scale. In some examples, the model 312 can includethe following equation for determining the Annular Pressure Loss:Annular Pressure Loss=(4*Wall Shear Stress*Length)/(Outer Diameter−InnerDiameter)where annular pressure loss is in psi; wall shear stress is in psi andincludes a fluid shear stress at a wall of an annulus of the wellbore;length is the length of the annulus of the wellbore in feet; outerdiameter is the outer diameter of the annulus of the wellbore in feet;and inner diameter is the inner diameter of the annulus of the wellborein feet.

In some examples, the computing device 120 can determine an input (e.g.,a value for a variable) for an equation (e.g., any of the aboveequations) based on sensor data 314 from a sensor (e.g., real-timesensor data from sensor 118), data input to the computing device 120 bya well operator, historical data about a well system, or any combinationof these. For example, the computing device 120 can receive sensorsignals from a mud scale indicative of a surface Mud Weight, extractsensor data 314 from the sensor signals, and store the sensor data 314in memory 308. The computing device 120 can retrieve the sensor data 314from memory 308 and use the sensor data 314 as an input to, for example,a Hydrostatic Pressure equation. As another example, the computingdevice 120 can receive input from a well operator (e.g., indicative ofan outer diameter of an annulus of a wellbore) and store the input asdata in memory 308. The computing device 120 can retrieve the data frommemory 308 and use the data as an input to, for example, an AnnularPressure Loss equation. As still another example, the computing device120 can receive historical data about a well system and store thehistorical data in memory 308. The computing device 120 can retrieve thehistorical data and use at least a portion of the historical data as aninput for an equation. In some examples, the computing device 120 cananalyze the historical data to determine new information about the wellsystem. The computing device 120 can use the new information as an inputfor an equation.

The memory 308 can also include sensor data 314 from a sensor 118. Thesensor 118 can measure a parameter (associated with the environment inthe well system or the wellbore operation) and transmit associatedsensor signals to the computing device 120. The computing device 120 canreceive the sensor signals via communication device 122, extract sensordata from the sensor signals, and store the sensor data 314 in memory308. Examples of the sensors 118 can include a pressure sensor, atemperature sensor, a microphone, an accelerometer, a depth sensor, aresistivity sensor, a vibration sensor, a fluid analyzer or detector, anultrasonic transducer, or any combination of these.

The memory 308 can also include one or more Δp values 318 (differencevalues). A Δp value 318 can be the difference between a predictedparameter (e.g., from the model 312) and a measured parameter (e.g.,from the sensor 118). For example, the computing device 120 can comparea predicted value of the parameter to a measured value of the parameterto determine a difference between the two values. The difference can bethe Δp value 318. The computing device can store the Δp value 318 inmemory 308.

The computing device 120 can be in electrical communication with thecommunication device 122. The communication device 122 can include orcan be coupled to an antenna 324. In some examples, part of thecommunication device 122 can be implemented in software. For example,the communication device 122 can include instructions stored in memory308.

The communication device 122 can receive signals from remote devices(e.g., sensor 118) and transmit data to remote devices. For example, totransmit data to a remote device, the processor 304 can transmit one ormore signals to the communication device 122. The communication device122 can receive the signals from the processor 304 and amplify, filter,modulate, frequency shift, and otherwise manipulate the signals. Thecommunication device 122 can transmit the manipulated signals to theantenna 324, which can responsively generate wireless signals that carrythe data.

In some examples, the communication device 122 can transmit data via awired interface. For example, the communication device 122 can transmitdata via a wireline. As another example, the communication device 122can generate an optical waveform. The communication device 122 cangenerate the optical waveform by pulsing a light emitting diode at aparticular frequency. The communication device 122 can transmit theoptical waveform via an optical cable (e.g., a fiber optic cable).

The computing device 120 can be in electrical communication with adisplay 310. The display 310 can receive signals from the processor 304and output one or more associated images. For example, the display 310can output a graph, such as the probability-mass distribution graphshown in FIGS. 5-17. Examples of the display 310 can include atelevision, a computer monitor, a liquid crystal display (LCD), or anyother suitable display device.

The computing device 120 is in electrical communication with a powersource 320. The power source 320 can additionally or alternatively be inelectrical communication with the communication device 122, the sensor118, or both. In some examples, the power source 320 can include abattery for powering the computing device 120, the communication device122, or the sensor 118. In other examples, power source 320 can includean electrical cable, such as a wireline, to which the computing device120 can be coupled.

In some examples, the power source 320 can include an AC signalgenerator. The computing device 120 can operate the power source 320 toapply a transmission signal to the antenna 324. For example, thecomputing device 120 can cause the power source 320 to apply a voltagewith a frequency within a specific frequency range to the antenna 324.This can cause the antenna 324 to generate a wireless transmission. Inother examples, the computing device 120, rather than the power source320, can apply the transmission signal to the antenna 324 for generatingthe wireless transmission.

FIG. 4 is a flow chart of an example of a process for determiningdifferences between downhole predictions and measured values accordingto some aspects.

In block 402, the computing device 120 predicts a value of a parameterassociated with a well environment, a wellbore operation, or both. Forexample, the computing device 120 can predict an equivalent circulatingdensity (ECD) of a fluid in a wellbore, a stand pipe pressure (SPP), orboth. The computing device 120 can use one or more models and apply oneor more constraints to the models to predict the parameter of the wellenvironment, the wellbore operation, or both. Examples of theconstraints can include a known type of fluid in the wellbore, a depthof the wellbore, a temperature of the wellbore, a location of thewellbore, a characteristic of a subterranean formation out of which thewellbore is drilled, or any combination of these. In some examples, auser can input the constraints into the computing device 120 and thecomputing device 120 can store the constraints in memory (e.g., memory308 of FIG. 3).

In block 404, the computing device 120 measures the value of theparameter (associated with the well environment, the wellbore operation,or both) using a sensor 118. The sensor 118 can detect one or morecharacteristics of the well environment, the wellbore operation (e.g.,the operation of a well tool), or both and transmit an associated sensorsignal to the computing device 120. The sensor signal can be an analogsignal or a digital signal. For example, the sensor 118 can detect apressure while drilling (PWD) during a drilling operation in a wellboreand transmit an associated sensor signal to the computing device 120.The computing device 120 can receive the sensor signal and extractsensor data from the sensor signal.

In block 406, the computing device 120 compares the predicted value ofthe parameter to the measured value of the parameter to determine adifference between the two. For example, the computing device 120 cansubtract (e.g., remove) the predicted ECD value from the measured PWDvalue to determine a difference between the two. As another example, thecomputing device 120 can subtract the predicted SPP value from themeasured SPP value to determine a difference between the two. Thecomputing device 120 can store the differences (e.g., between thepredicted ECD value and the measured PWD value, between the predictedSPP value and the measured SPP value, or both) in memory.

In block 408, the computing device 120 generates a visual interface thatincludes a data point representative of the difference (between thepredicted value of the parameter and the measured value of theparameter) plotted on a probability-mass distribution graph. Thecomputing device 120 can output the visual interface on a display (e.g.,display 310 of FIG. 3). A well operator can visually inspect theprobability-mass distribution graph determine the accuracy of thepredicted value of the parameter.

For example, FIG. 5 shows an example of a probability-mass distributioncurve 502 generated using multiple predicted ECD values. FIG. 6 shows anexample of a probability-mass distribution curve 602 generated usingmultiple predicted SPP values. The data point can be plotted along theprobability-mass distribution curve 502, 602. The process can return toblock 402 and iterate the steps of blocks 402-408 to generate multipledata points and plot the data points on the probability-massdistribution curve 502, 602. In some examples, the data points can havea substantially normal distribution. The probability-mass distributioncurve 502, 602 can represent the accuracy of predicted values associatedwith the data points. A well operator can visually inspect theprobability-mass distribution curve 502, 602 to determine the accuracyof the predicted values.

In some examples, the computing device 120 can tune the predicted valueof the parameter for specific wellbore operations. For example, FIGS.7-12 show probability-mass distribution graphs generated using predictedECD values that are tuned for various wellbore operations. FIGS. 13-17show probability-mass distribution graphs generated using predicted SPPvalues that are tuned for various wellbore operations.

In some examples, the computing device 120 can analyze aprobability-mass distribution graph, the associated data points, or bothto determine a source of a difference between the predicted value of theparameter and the measured parameter. For example, the computing device120 can determine that the difference is due to an event occurring inthe wellbore, an error with a model that generated the predicted valueof the parameter, an error with data input by a user, an equipmentfailure, or any combination of these. The computing device 120, the welloperator, or both can perform one or more processes or tasks based onthe source of the difference. The processes or tasks can be selected tocorrect the error or otherwise reduce the difference between thepredicted parameter and the measured parameter.

FIG. 18 is a flow chart of an example of a process for determiningsources of erroneous downhole predictions according to some aspects.

In block 1802, the computing device 120 determines a first trendindicated by multiple predicted values of a parameter and occurringduring a time interval. For example, the computing device 120 cananalyze the multiple predicted values to determine that the predictedvalues are increasing, staying substantially the same, or decreasingover the time interval.

In block 1804, the computing device 120 determines a second trendindicated by multiple measured values (e.g., detected by a sensor) andoccurring during the time interval. For example, the computing device120 can analyze the multiple measured values to determine that thepredicted values are increasing, staying substantially the same, ordecreasing over the time interval.

In block 1806, the computing device 120 determines if the first trend issimilar to the second trend. The first trend can be similar to thesecond trend if one or more characteristics of the first trend arewithin a predefined threshold of one or more characteristics of thesecond trend. For example, if the first trend includes the predictedvalues increasing by an amount less than a threshold, and the secondtrend includes the measured values increasing by another amount lessthan the threshold, the first trend can be similar to the second trend.

In some examples, the computing device 120 can determine if the firsttrend is similar to the second trend based on a difference between oneor more of the predicted values and one or more of the measured values.For example, the computing device 120 can determine if a differencebetween a predicted value and a measured value exceeds a threshold. Ifthe difference exceeds the threshold, the computing device 120 candetermine that the first trend is not similar to the second trend. Insome examples, the computing device 120 can additionally oralternatively determine if a rate of change of a difference betweenmultiple predicted values and multiple measured values exceeds athreshold. If the rate of change of the difference exceeds thethreshold, the computing device 120 can determine that the first trendis not similar to the second trend.

If the first trend is similar to the second trend, the process canreturn to block 1802. If the first trend is different from the secondtrend, the process can continue to block 1808.

In block 1808, the computing device 120 determines if an operationalmode of a well tool changed. In some examples, an operational mode caninclude a mode of operation or a status of a well tool in a wellbore.For example, an operational mode can include a drilling status of arotary drilling tool in the wellbore. In some examples, an operationalmode can include performing rotary drilling, performing slide drilling,circulation or hole cleaning, making a connection between two wellcomponents, idling, tripping in, tripping out, or any combination ofthese.

The computing device 120 can determine the operational mode based onsensor data from one or more sensors (e.g., proximate to the wellbore).Examples of the sensor data can include a depth of a drill bit in awellbore, a depth of the wellbore, a pump rate, a rotation speed of apipe, a translation speed of a pipe, or any combination of these. Forexample, the computing device 120 can determine that the operationalmode includes drilling in response to detecting, using one or moresensors, that a depth of a drill bit in a wellbore is within one foot ofa bottom of the wellbore, a rotation rate of a drill tool is greaterthan zero, a flow rate of a fluid is greater than zero, or anycombination of these.

If the computing device 120 determines that the operational modechanged, the process continues to block 1814. Otherwise, the processcontinues to block 1810.

In block 1810, the computing device 120 determines if an input parameter(e.g., a parameter provided to a model executing on the computing device120 by a user or sensor) changed. For example, the computing device 120can compare the input parameter during the time interval to the inputparameter during a previous time interval to determine a differencebetween the two. If there is a difference between the two, the computingdevice 120 can determine that the input parameter changed.

If the computing device 120 determines that an input parameter changed,the process continues to block 1814. Otherwise, the process continues toblock 1812.

In block 1812, the computing device 120 outputs an error notification.The error notification can include a sound, visual effect, a tactileeffect, or any combination of these. In some examples, the errornotification can alert the well operator that there are divergent trendsbetween the predicted values of a parameter and the measured values ofthe parameter. The error notification can additionally or alternativelyindicate that the difference is not due to a change in an operationalmode (e.g., of a well tool), an input parameter, or both.

In block 1814, the computing device 120 determines a trigger causing thedifference between the first trend and the second trend based on achange in an operational mode, a change in an input parameter, or both.In some examples, the computing device 120 can implement one or moreworkflows, processes, or tasks to determine the trigger. For example,the first trend, the second trend, or both can be related to or dependon the operational mode, the input parameter, or both. The computingdevice 120 can include (e.g., stored in memory 308 of FIG. 3) one ormore lookup tables and/or algorithms indicating or representing knownrelationships between predicted values, measured values, inputparameters, and/or operational modes. The computing device 120 can usethe lookup tables or algorithms to determine if any known relationshipshave been violated. In response to detecting that a known relationshiphas been violated, the computing device 120 can determine that thetrigger includes, at least in part, the violation.

For example, the set of predicted values can include ECD predictionsthat increased over a time period and the set of measured values caninclude PWD values that remained substantially constant over the timeperiod. To determine a trigger causing the difference between the ECDpredictions and the PWD values, the computing device 120 can determineif an operational mode changed or an input parameter changed (e.g., asdiscussed with respect to blocks 1808 and 1810). The computing device120 can determine that an input parameter including a temperature valueincreased during the time period. The computing device 120 can use alookup table to determine that there is a proportional relationshipbetween temperature values and PWD values. The computing device 120 canfurther determine that the combination of substantially constant PWDvalues and increasing temperature values during the time period violatesthe proportional relationship. This violation can indicate that there isan error in the measured PWD values or the temperature values. In someexamples, the computing device 120 can be unable to determine an exacttrigger, but can identify multiple potential triggers (e.g., such as theerror being in the measured PWD values or the temperature values).

In other examples, the computing device 120 can determine the exacttrigger. For example, the computing device 120 can determine a set ofpredicted values that includes ECD predictions that increased over atime period, a set of measured values that includes PWD values thatremained substantially constant over the time period, that an inputparameter (e.g., a mud density temperature input by a user) changed overthe time period, and that the operational mode remained substantiallyconstant over the time period. In some examples, the computing device120 can determine that the increasing ECD predictions were triggered byan incorrect input parameter. The computing device 120 can determinethat the increasing ECD predictions were triggered by the incorrectinput parameter, because the input parameter changed over the timeperiod, while the measured PWD values and the operational mode remainedsubstantially constant.

In block 1816, the computing device 120 determines a source of thetrigger (e.g., to determine a source of the difference between the firsttrend in the second trend). In some examples, the computing device 120can perform the process shown in FIG. 19 to determine the source of thetrigger.

Referring now to FIG. 19, in block 1902, the computing device 120determines if the trigger occurred due to an erroneous user input. Forexample, if the trigger is an input parameter including an erroneoustemperature measurement, the computing device 120 can determine if theerroneous temperature measurement was provided by a user. In someexamples, the computing device 120 can determine if the input parameterwas provided by a user or by a sensor. For example, the computing device120 can perform a system check to determine if a temperature sensor iscoupled to the computing device 120. The computing device 120 candetermine that the input parameter was provided by a user in response todetecting the absence of the temperature sensor, or that the inputparameter was provided by the sensor in response to detecting thepresence of the temperature sensor.

In some examples, the computing device 120 can prompt the user todetermine whether an input parameter was provided by the user or anothersource. For example, the computing device 120 can prompt the user (e.g.,via a graphical user interface component) to provide input indicatingwhether the input parameter was provided by the user or another source(e.g., a sensor).

If the computing device 120 determines that trigger occurred due to theerroneous user input, the process continues to block 1910. Otherwise,the process continues to block 1904.

In block 1904, the computing device 120 determines if the triggeroccurred due to equipment failure. For example, if the trigger is aninput parameter including an erroneous temperature measurement, thecomputing device 120 can determine if the erroneous temperaturemeasurement was provided by hardware (e.g., a sensor 118 of FIG. 3). Insome examples, the computing device 120 can determine if the inputparameter was provided by a user or by a sensor. For example, thecomputing device 120 can perform a system check to determine if atemperature sensor is coupled to the computing device 120. The computingdevice 120 can determine that the input parameter was provided by thetemperature sensor in response to detecting the presence of thetemperature sensor, or that the input parameter was not provided by thetemperature sensor in response to detecting the absence of thetemperature sensor. Thus, the computing device 120 can determine thatthe trigger was due to a faulty temperature sensor.

In some examples, the computing device 120 can prompt the user todetermine whether an input parameter was provided by hardware or anothersource. For example, the computing device 120 can prompt the user (e.g.,via a graphical user interface component) to provide input indicatingwhether the input parameter was provided by hardware or the user.

In some examples, the equipment failure can include an error in a modelexecuting on the computing device 120. In some examples, the computingdevice 120 can determine if there is an error in the model by analyzingΔp values associated with two or more different wellbore operations. Forexample, the computing device 120 can determine that a set of Δp valuesassociated with a tripping operation are larger than another set of Δpvalues associated with a drilling operation. The computing device 120can further determine that no event is occurring in the wellbore andthat the user has input all data correctly into the model. Thecombination of these determinations can indicate that there is an errorin the model.

If the computing device 120 determines that trigger occurred due toequipment failure, the process continues to block 1910. Otherwise, theprocess continues to block 1906.

In block 1906, the computing device 120 determines if the triggeroccurred due to a wellbore event. The computing device 120 can determineif the event occurred based on sensor data (e.g., from sensor 118 ofFIG. 3). For example, the computing device 120 can determine if anamount of pressure in the wellbore, a temperature in the wellbore, oranother environmental condition in the wellbore has changed in an amountabove a threshold. In some examples, the event can include a well tooloperating or failing to operate in a particular manner. For example, theevent can include a bit nozzle for a drilling tool becoming blocked.

In some examples, the computing device 120 can determine if the eventoccurred by analyzing Δp values associated with two or more differentwellbore operations (e.g., stored in memory 308 of FIG. 3). For example,the computing device 120 can determine a first Δp value on the SPPprobability-mass distribution graph of FIG. 6. The first Δp value cansubstantially deviate from the probability-mass distribution curve 602.The computing device 120 can substantially simultaneously determine asecond Δp value on the ECD probability-mass distribution graph of FIG.5. The second Δp values can substantially adhere to the probability-massdistribution curve 502. The deviation of the first Δp value from theprobability-mass distribution curve 602 in conjunction with theadherence of the second Δp value with the probability-mass distributioncurve 502 can indicate a particular downhole event has occurred. Forexample, the computing device 120 can determine that, based on thedeviation of the first Δp value from the probability-mass distributioncurve 602, and the conformity of the second Δp value with theprobability-mass distribution curve 502, there is a blocked bit nozzle.

If the computing device 120 determines that trigger occurred due to awellbore event, the process continues to block 1910. Otherwise, theprocess continues to block 1908.

In block 1908, the computing device 120 outputs an error notification.The error notification can include a sound, visual effect, a tactileeffect, or any combination of these. In some examples, the errornotification can alert the well operator that the trigger is not due toan erroneous user input, an equipment failure, and/or a wellbore event.The error notification can indicate that the source of the error isunknown.

In block 1910, the computing device 120 outputs a source of the trigger.For example, the computing device 120 can output a notification. Thenotification can indicate that the source of the trigger includes anerroneous user input, an equipment failure, and/or a wellbore event. Thenotification can additionally or alternatively include more specificinformation regarding the source of the trigger (e.g., the specificerroneous user input, piece of equipment that failed, or wellbore eventthat occurred).

In block 1912, the computing device 120 determines a process or task forreducing a difference between a first trend associated with a set ofpredicted values and a second trend associated with a set of measuredvalues (e.g., from FIG. 18). The computing device 120 then implementsthe process or task.

For example, if the difference between the first trend and the secondtrend is due to incorrect data input by the user, the process or taskcan include the computing device 120 prompting the user for new data.The computing device 120 can receive the new data from the user andapply the new data to the model. As another example, if the differencebetween the first trend and the second trend is due to an eventoccurring in the wellbore, the computing device 120 can modify aparameter of the model to account for the event, execute a new model,prompt the user for action, or any combination of these. As stillanother example, if the difference between the first trend and thesecond trend is due to an error in the model, the computing device 120can modify a parameter of the model, execute a new model, alert the userto the error in the model, or any combination of these.

In some aspects, systems, methods, and computer-readable media fordetermining sources of erroneous downhole predictions are providedaccording to one or more of the following examples:

EXAMPLE #1

A system for use in a wellbore can include a computing device includinga processing device and a memory device in which instructions executableby the processing device are stored. The instructions can be for causingthe processing device to generate, using a model, multiple predictedvalues of a first parameter associated with a well environment or with awellbore operation. The instructions can be for causing the processingdevice to determine a first trend indicated by the multiple predictedvalues and occurring during a time period. The instructions can be forcausing the processing device to receive, from a sensor, multiplemeasured values of a second parameter associated with the wellenvironment or with the wellbore operation. The instructions can be forcausing the processing device to determine a second trend indicated bythe multiple measured values and occurring during the time period. Theinstructions can be for causing the processing device to determine adifference between the first trend and the second trend or a rate ofchange of the difference between the first trend and the second trend.The instructions can be for causing the processing device to, inresponse to the difference exceeding a first threshold or the rate ofchange exceeding a second threshold, determine a source of thedifference including at least one of an erroneous user input, anequipment failure, a wellbore event, or a model error.

EXAMPLE #2

The system of Example #1 may feature the memory device includinginstructions executable by the processing device for causing theprocessing device to determine the source of the difference bydetermining a trigger causing the difference between the first trend andthe second trend using a lookup table.

EXAMPLE #3

The system of Example #2 may feature the memory device includinginstructions executable by the processing device for causing theprocessing device to determine the trigger causing the differencebetween the first trend and the second trend using the lookup table by:determining, using the lookup table, a relationship between at least twoof the first parameter, the second parameter, and a third parameterassociated with the well environment or with the wellbore operation; anddetermining the trigger includes a violation of the relationship betweenthe at least two of the first parameter, the second parameter, and thethird parameter. The memory device can also include instructionsexecutable by the processing device for causing the processing device todetermine that the source of the difference is the erroneous user input,the equipment failure, the wellbore event, or the model error based onthe trigger.

EXAMPLE #4

The system of any of Examples #1-3 may feature the memory deviceincluding instructions executable by the processing device for causingthe processing device to determine that the source of the differenceincludes the erroneous user input in response to determining that aninput parameter usable by the model to generate the multiple predictedvalues was provided by a user; or determine that the source of thedifference includes the equipment failure in response to determiningthat the input parameter usable by the model to generate the multiplepredicted values was provided by another sensor.

EXAMPLE #5

The system of any of Examples #1-4 may feature the memory deviceincluding instructions executable by the processing device for causingthe processing device to determine that the source of the differenceincludes the wellbore event based on a change in data from anothersensor positioned proximately to the wellbore.

EXAMPLE #6

The system of any of Examples #1-5 may feature the memory deviceincluding instructions executable by the processing device for causingthe processing device to output the source of the difference between thefirst trend and the second trend; select a process for reducing thedifference between the first trend and the second trend based on thesource of the difference; and implement the process.

EXAMPLE #7

The system of any of Examples #1-6 may feature the wellbore eventincluding a change in an environmental condition in the wellbore, a welltool operating in a specific manner, or the well tool failing to operatein a particular manner.

EXAMPLE #8

A method can include generating, using a model, multiple predictedvalues of a first parameter associated with a well environment or with awellbore operation. The method can include determining a first trendindicated by the multiple predicted values and occurring during a timeperiod. The method can include receiving, from a sensor, multiplemeasured values of a second parameter associated with the wellenvironment or with the wellbore operation. The method can includedetermining a second trend indicated by the multiple measured values andoccurring during the time period. The method can include determining adifference between the first trend and the second trend or a rate ofchange of the difference between the first trend and the second trend.The method can include, in response to the difference exceeding a firstthreshold or the rate of change exceeding a second threshold,determining a source of the difference including at least one of anerroneous user input, an equipment failure, a wellbore event, or a modelerror.

EXAMPLE #9

The method of Example #8 may feature determining the source of thedifference by determining a trigger causing the difference between thefirst trend and the second trend using a lookup table.

EXAMPLE #10

The method of Example #9 may feature determining the trigger causing thedifference between the first trend and the second trend using the lookuptable by: determining, using the lookup table, a relationship between atleast two of the first parameter, the second parameter, and a thirdparameter associated with the well environment or with the wellboreoperation; and determining the trigger includes a violation of therelationship between the at least two of the first parameter, the secondparameter, and the third parameter. The method may also featuredetermining that the source of the difference is the erroneous userinput, the equipment failure, the wellbore event, or the model errorbased on the trigger.

EXAMPLE #11

The method of any of Examples #8-10 may feature determining that thesource of the difference includes the erroneous user input in responseto determining that an input parameter usable by the model to generatethe multiple predicted values was provided by a user; or determiningthat the source of the difference includes the equipment failure inresponse to determining that the input parameter usable by the model togenerate the multiple predicted values was provided by another sensor.

EXAMPLE #12

The method of any of Examples #8-11 may feature determining that thesource of the difference includes the wellbore event based on a changein data from another sensor positioned proximately to a wellbore,wherein the wellbore event includes another change in an environmentalcondition in the wellbore, a well tool operating in a specific manner,or the well tool failing to operate in a particular manner.

EXAMPLE #13

The method of any of Examples #8-12 may feature outputting the source ofthe difference between the first trend and the second trend; selecting aprocess for reducing the difference between the first trend and thesecond trend based on the source of the difference; and implementing theprocess.

EXAMPLE #14

A non-transitory computer readable medium can include program code thatis executable by a processor to cause the processor to generate, using amodel, multiple predicted values of a first parameter associated with awell environment or with a wellbore operation. The program code cancause the processor to determine a first trend indicated by the multiplepredicted values and occurring during a time period. The program codecan cause the processor to receive, from a sensor, multiple measuredvalues of a second parameter associated with the well environment orwith the wellbore operation. The program code can cause the processor todetermine a second trend indicated by the multiple measured values andoccurring during the time period. The program code can cause theprocessor to determine a difference between the first trend and thesecond trend or a rate of change of the difference between the firsttrend and the second trend. The program code can cause the processor to,in response to the difference exceeding a first threshold or the rate ofchange exceeding a second threshold, determine a source of thedifference including at least one of an erroneous user input, anequipment failure, a wellbore event, or a model error.

EXAMPLE #15

The non-transitory computer readable medium of Example #14 may featureprogram code that is executable by the processor to cause the processorto determine the source of the difference by determining a triggercausing the difference between the first trend and the second trendusing a lookup table.

EXAMPLE #16

The non-transitory computer readable medium of Example #15 may featureprogram code that is executable by the processor to cause the processorto determine the trigger causing the difference between the first trendand the second trend using the lookup table by: determining, using thelookup table, a relationship between at least two of the firstparameter, the second parameter, and a third parameter associated withthe well environment or with the wellbore operation; and determining thetrigger includes a violation of the relationship between the at leasttwo of the first parameter, the second parameter, and the thirdparameter. The program code can also cause the processor to determinethat the source of the difference is the erroneous user input, theequipment failure, the wellbore event, or the model error based on thetrigger.

EXAMPLE #17

The non-transitory computer readable medium of any of Examples #14-16may feature program code that is executable by the processor to causethe processor to determine that the source of the difference includesthe erroneous user input in response to determining that an inputparameter usable by the model to generate the multiple predicted valueswas provided by a user; or determine that the source of the differenceincludes the equipment failure in response to determining that the inputparameter usable by the model to generate the multiple predicted valueswas provided by another sensor.

EXAMPLE #18

The non-transitory computer readable medium of any of Examples #14-17may feature program code that is executable by the processor to causethe processor to determine that the source of the difference includesthe wellbore event based on a change in data from another sensorpositioned proximately to a wellbore.

EXAMPLE #19

The non-transitory computer readable medium of any of Examples #14-18may feature program code that is executable by the processor to causethe processor to output the source of the difference between the firsttrend and the second trend; select a process for reducing the differencebetween the first trend and the second trend based on the source of thedifference; and implement the process.

EXAMPLE #20

The non-transitory computer readable medium of any of Examples #14-19may feature the wellbore event including a change in an environmentalcondition in a wellbore, a well tool operating in a specific manner, orthe well tool failing to operate in a particular manner.

The foregoing description of certain examples, including illustratedexamples, has been presented only for the purpose of illustration anddescription and is not intended to be exhaustive or to limit thedisclosure to the precise forms disclosed. Numerous modifications,adaptations, and uses thereof will be apparent to those skilled in theart without departing from the scope of the disclosure.

What is claimed is:
 1. A system for use in a wellbore, the systemcomprising: a computing device including a processing device and amemory device, the memory device storing instructions which whenexecuted by the processing device are configured for causing theprocessing device to: generate, using a model, a plurality of predictedvalues of a first parameter associated with a well environment or with awellbore operation; determine a first trend indicated by the pluralityof predicted values and occurring during a time period; receive, from asensor, a plurality of measured values of a second parameter associatedwith the well environment or with the wellbore operation; determine asecond trend indicated by the plurality of measured values and occurringduring the time period; determine a difference between the first trendand the second trend or a rate of change of the difference between thefirst trend and the second trend; determine a trigger associated withthe difference between the first trend and the second trend by:determining a relationship between at least two parameters among (i) thefirst parameter, (ii) the second parameter, and (iii) a third parameterassociated with the well environment or with the wellbore operation; anddetermining that the trigger comprises a violation of the relationship;and in response to the difference exceeding a first threshold or therate of change exceeding a second threshold, determine, based on thetrigger, that a source of the difference comprises at least one of anerroneous user input, an equipment failure, a wellbore event, or a modelerror.
 2. The system of claim 1, wherein the instructions are furtherconfigured for causing the processing device to: determine therelationship by using a lookup table.
 3. The system of claim 1, whereinthe instructions are further configured for causing the processingdevice to: determine that the source of the difference comprises theerroneous user input in response to determining that an input parameterused by the model to generate the plurality of predicted values wasprovided by a user.
 4. The system of claim 1, wherein the sensor is afirst sensor, and wherein the instructions are further configured forcausing the processing device to: determine that the source of thedifference comprises the wellbore event based on a change in data from asecond sensor positioned proximately to the wellbore, the second sensorbeing different from the first sensor.
 5. The system of claim 1, whereinthe instructions are further configured for causing the processingdevice to: select, based on the source of the difference, a process forreducing the difference between the first trend and the second trend;and implement the process.
 6. The system of claim 1, wherein thewellbore event comprises a change in an environmental condition in thewellbore, a well tool operating in a specific manner, or the well toolfailing to operate in a particular manner.
 7. A method comprising:generating, by a processor and using a model, a plurality of predictedvalues of a first parameter associated with a well environment or with awellbore operation; determining, by the processor, a first trendindicated by the plurality of predicted values and occurring during atime period; receiving, by the processor and from a sensor, a pluralityof measured values of a second parameter associated with the wellenvironment or with the wellbore operation; determining, by theprocessor, a second trend indicated by the plurality of measured valuesand occurring during the time period; determining, by the processor, adifference between the first trend and the second trend or a rate ofchange of the difference between the first trend and the second trend;determining, by the processor, a trigger associated with the differencebetween the first trend and the second trend, wherein the triggerincludes a violation of a relationship between at least two parametersamong (i) the first parameter, (ii) the second parameter, and (iii) athird parameter associated with the well environment or with thewellbore operation; and in response to the difference exceeding a firstthreshold or the rate of change exceeding a second threshold,determining, based on the trigger, a source of the difference comprisingat least one of an erroneous user input, an equipment failure, awellbore event, or a model error.
 8. The method of claim 7, furthercomprising determining the relationship using a lookup table.
 9. Themethod of claim 7, wherein the sensor is a first sensor, and furthercomprising: determining that the source of the difference comprises theequipment failure in response to determining that an input parameterused by the model to generate the plurality of predicted values wasprovided by a second sensor that is different from the first sensor. 10.The method of claim 7, wherein the sensor is a first sensor, and furthercomprising determining that the source of the difference comprises thewellbore event based on a change in data from a second sensor positionedproximately to a wellbore, wherein the wellbore event comprises anotherchange in an environmental condition in the wellbore, a well tooloperating in a specific manner, or the well tool failing to operate in aparticular manner.
 11. The method of claim 7, further comprising:selecting, based on the source of the difference, a process for reducingdifferences between the first trend and the second trend; andimplementing the process.
 12. A non-transitory computer readable mediumstoring program code, which when executed by a processor is configuredto cause the processor to: generate, using a model, a plurality ofpredicted values of a first parameter associated with a well environmentor with a wellbore operation; determine a first trend indicated by theplurality of predicted values and occurring during a time period;receive, from a sensor, a plurality of measured values of a secondparameter associated with the well environment or with the wellboreoperation; determine a second trend indicated by the plurality ofmeasured values and occurring during the time period; determine adifference between the first trend and the second trend or a rate ofchange of the difference between the first trend and the second trend;determine a trigger associated with the difference between the firsttrend and the second trend, wherein the trigger includes a violation ofa relationship between at least two parameters among (i) the firstparameter, (ii) the second parameter, and (iii) a third parameterassociated with the well environment or with the wellbore operation; andin response to the difference exceeding a first threshold or the rate ofchange exceeding a second threshold, determine, based on the trigger, asource of the difference comprising at least one of an erroneous userinput, an equipment failure, a wellbore event, or a model error.
 13. Thenon-transitory computer readable medium of claim 12, wherein the programcode is executable by the processor to cause the processor to: determinethe relationship using a lookup table.
 14. The non-transitory computerreadable medium of claim 12, wherein the program code is executable bythe processor to cause the processor to: determine that the source ofthe difference comprises the erroneous user input in response todetermining that an input parameter usable by the model to generate theplurality of predicted values was provided by a user; or determine thatthe source of the difference comprises the equipment failure in responseto determining that the input parameter usable by the model to generatethe plurality of predicted values was provided by another sensor. 15.The non-transitory computer readable medium of claim 12, wherein thesensor is a first sensor, and wherein the program code is executable bythe processor to cause the processor to: determine that the source ofthe difference comprises the wellbore event based on a change in datafrom a second sensor positioned proximately to a wellbore, the secondsensor being different from the first sensor.
 16. The non-transitorycomputer readable medium of claim 12, wherein the program code isexecutable by the processor to cause the processor to: select, based onthe source of the difference, a process for reducing differences betweenthe first trend and the second trend; and implement the process.
 17. Thenon-transitory computer readable medium of claim 12, wherein thewellbore event comprises a change in an environmental condition in awellbore, a well tool operating in a specific manner, or the well toolfailing to operate in a particular manner.
 18. The system of claim 1,wherein the instructions are further configured to output the source ofthe difference between the first trend and the second trend on adisplay.
 19. The method of claim 7, further comprising outputting thesource of the difference between the first trend and the second trend ona display.
 20. The non-transitory computer readable medium of claim 12,wherein the program code is executable by the processor to cause theprocessor to output the source of the difference between the first trendand the second trend on a display.